Journal of Bioinformatics and Computational Biology

Antimicrobial peptides (AMPs), as the preferred alternatives to antibiotics, have wide application with good prospects. Identifying AMPs through wet lab experiments remains expensive, time-consuming and challenging. Many machine learning methods have been proposed to predict AMPs and achieved good results. In this work, we combine two kinds of word embedding features with the statistical features of peptide sequences to develop an ensemble classifier, named EnAMP, in which, two deep neural networks are trained based on Word2vec and Glove word embedding features of peptide sequences, respectively, meanwhile, we utilize statistical features of peptide sequences to train random forest and support vector machine classifiers...

The accurate identification of drug-target affinity (DTA) is crucial for advancements in drug discovery and development. Many deep learning-based approaches have been devised to predict drug-target binding affinity accurately, exhibiting notable improvements in performance. However, the existing prediction methods often fall short of capturing the global features of proteins. In this study, we proposed a novel model called ETransDTA, specifically designed for predicting drug-target binding affinity. ETransDTA combines convolutional layers and transformer, allowing for the simultaneous extraction of both global and local features of target proteins...

Identifying valuable features from complex omics data is of great significance for disease diagnosis study. This paper proposes a new feature selection algorithm based on sample network (FS-SN) to mine important information from omics data. The sample network is constructed according to the sample neighbor relationship at the molecular (feature) expression level, and the distinguishing ability of the feature is evaluated based on the topology of the sample network. The sample network established on a feature with a strong discriminating ability tends to have many edges between the same group samples and few edges between the different group samples...

In this paper, we propose a novel approach for predicting the activity/inactivity of molecules with the BRCA1 gene by combining pharmacophore modeling and deep learning techniques. Initially, we generated 3D pharmacophore fingerprints using a pharmacophore model, which captures the essential features and spatial arrangements critical for biological activity. These fingerprints served as informative representations of the molecular structures. Next, we employed deep learning algorithms to train a predictive model using the generated pharmacophore fingerprints...

Identifying proteins is crucial for disease diagnosis and treatment. With the increase of known proteins, large-scale batch predictions are essential. However, traditional biological experiments being time-consuming and expensive are difficult to accomplish this task efficiently. Nevertheless, deep learning algorithms based on big data analysis have manifested potential in this aspect. In recent years, language representation models, especially BERT, have made significant advancements in natural language processing...

Single-cell RNA sequencing (scRNA-seq) has been proven to be an effective technology for investigating the heterogeneity and transcriptome dynamics due to the single-cell resolution. However, one of the major problems for data obtained by scRNA-seq is excessive zeros in the count matrix, which hinders the downstream analysis enormously. Here, we present a method that integrates non-negative matrix factorization and transfer learning (NMFTL) to impute the scRNA-seq data. It borrows gene expression information from the additional dataset and adds graph-regularized terms to the decomposed matrices...

Copy number variation (CNV), as a type of genomic structural variation, accounts for a large proportion of structural variation and is related to the pathogenesis and susceptibility to some human diseases, playing an important role in the development and change of human diseases. The development of next-generation sequencing technology (NGS) provides strong support for the design of CNV detection algorithms. Although a large number of methods have been developed to detect CNVs using NGS data, it is still considered a difficult problem to detect CNVs with low purity and coverage...

Given several number sequences, determining the longest common subsequence is a classical problem in computer science. This problem has applications in bioinformatics, especially determining transposable genes. Nevertheless, related works only consider how to find one longest common subsequence. In this paper, we consider how to determine the uniqueness of the longest common subsequence. If there are multiple longest common subsequences, we also determine which number appears in all/some/none of the longest common subsequences...

Some biomedical datasets contain a small number of samples which have large numbers of features. This can make analysis challenging and prone to errors such as overfitting and misinterpretation. To improve the accuracy and reliability of analysis in such cases, we present a tutorial that demonstrates a mathematical approach for a supervised two-group classification problem using two medical datasets. A tutorial provides insights on effectively addressing uncertainties and handling missing values without the need for removing or inputting additional data...

The polyproline-II (PPII) structure domain is crucial in organisms' signal transduction, transcription, cell metabolism, and immune response. It is also a critical structural domain for specific vital disease-associated proteins. Recognizing PPII is essential for understanding protein structure and function. To accurately predict PPII in proteins, we propose a novel method, AAindex-PPII, which only adopts amino acid index to characterize protein sequences and uses a Bidirectional Gated Recurrent Unit (BiGRU)-Improved TextCNN composite deep learning model to predict PPII in proteins...

O-glycosylation (Oglyc) plays an important role in various biological processes. The key to understanding the mechanisms of Oglyc is identifying the corresponding glycosylation sites. Two critical steps, feature selection and classifier design, greatly affect the accuracy of computational methods for predicting Oglyc sites. Based on an efficient feature selection algorithm and a classifier capable of handling imbalanced datasets, a new computational method, ChiMIC-based balanced decision table O-glycosylation (CBDT-Oglyc), is proposed...

Defining meaningful feature (molecule) combinations can enhance the study of disease diagnosis and prognosis. However, feature combinations are complex and various in biosystems, and the existing methods examine the feature cooperation in a single, fixed pattern for all feature pairs, such as linear combination. To identify the appropriate combination between two features and evaluate feature combination more comprehensively, this paper adopts kernel functions to study feature relationships and proposes a new omics data analysis method KF-[Formula: see text]-TSP...

Various diseases, including Huntington's disease, Alzheimer's disease, and Parkinson's disease, have been reported to be linked to amyloid. Therefore, it is crucial to distinguish amyloid from non-amyloid proteins or peptides. While experimental approaches are typically preferred, they are costly and time-consuming. In this study, we have developed a machine learning framework called iAMY-RECMFF to discriminate amyloidgenic from non-amyloidgenic peptides. In our model, we first encoded the peptide sequences using the residue pairwise energy content matrix...

The evolution of single-cell technology is ongoing, continually generating massive amounts of data that reveal many mysteries surrounding intricate diseases. However, their drawbacks continue to constrain us. Among these, annotating cell types in single-cell gene expressions pose a substantial challenge, despite the myriad of tools at our disposal. The rapid growth in data, resources, and tools has consequently brought about significant alterations in this area over the years. In our study, we spotlight all note-worthy cell type annotation techniques developed over the past four years...

Over the past decades, many existing drugs and clinical/preclinical compounds have been repositioned as new therapeutic indication from which they were originally intended and to treat off-target diseases by targeting their noncognate protein receptors, such as Sildenafil and Paxlovid, termed drug repurposing (DRP). Despite its significant attraction in the current medicinal community, the DRP is usually considered as a matter of accidents that cannot be fulfilled reliably by traditional drug discovery protocol...

Metabolic pathways play a crucial role in understanding the biochemistry of organisms. In metabolic pathways, modules refer to clusters of interconnected reactions or sub-networks representing specific functional units or biological processes within the overall pathway. In pathway modules, compounds are major elements and refer to the various molecules that participate in the biochemical reactions within the pathway modules. These molecules can include substrates, intermediates and final products. Determining the presence relation of compounds and pathway modules is essential for synthesizing new molecules and predicting hidden reactions...

Usually, the clustering process is the first step in several data analyses. Clustering allows identify patterns we did not note before and helps raise new hypotheses. However, one challenge when analyzing empirical data is the presence of covariates, which may mask the obtained clustering structure. For example, suppose we are interested in clustering a set of individuals into controls and cancer patients. A clustering algorithm could group subjects into young and elderly in this case. It may happen because the age at diagnosis is associated with cancer...

Cancer is characterized by the dysregulation of alternative splicing (AS). However, the comprehensive regulatory mechanisms of AS in lung adenocarcinoma (LUAD) are poorly understood. Here, we displayed the AS landscape in LUAD based on the integrated analyses of LUAD's multi-omics data. We identified 13,995 AS events in 6309 genes as differentially expressed alternative splicing events (DEASEs) mainly covering protein-coding genes. These DEASEs were strongly linked to "cancer hallmarks", such as apoptosis, DNA repair, cell cycle, cell proliferation, angiogenesis, immune response, generation of precursor metabolites and energy, p53 signaling pathway and PI3K-AKT signaling pathway...

Most of the functional RNA elements located within large transcripts are local. Local folding therefore serves a practically useful approximation to global structure prediction. Due to the sensitivity of RNA secondary structure prediction to the exact definition of sequence ends, accuracy can be increased by averaging local structure predictions over multiple, overlapping sequence windows. These averages can be computed efficiently by dynamic programming. Here we revisit the local folding problem, present a concise mathematical formalization that generalizes previous approaches and show that correct Boltzmann samples can be obtained by local stochastic backtracing in McCaskill's algorithms but not from local folding recursions...

Drug synergy has emerged as a viable treatment option for malignancy. Drug synergy reduces toxicity, improves therapeutic efficacy, and overcomes drug resistance when compared to single-drug doses. Thus, it has attained significant interest from academics and pharmaceutical organizations. Due to the enormous combinatorial search space, it is impossible to experimentally validate every conceivable combination for synergistic interaction. Due to advancement in artificial intelligence, the computational techniques are being utilized to identify synergistic drug combinations, whereas prior literature has focused on treating certain malignancies...